RealTime Object Detection for
Surveillance in Retail Stores
A Study on Enhancing Retail Security and Operations
Introduction
• Definition:
• Object detection refers to the process of identifying and locating objects within
images or video streams.
• In a retail context, this involves identifying people, products, and activities in real
time.
• Key Characteristics:
• Real-Time Processing: Immediate analysis and alerts.
• Object Localization & Classification: Detects objects (e.g., person, product) and
places bounding boxes around them.
How Real-Time Object Detection Works in
Retail
Surveillance
• Detection Pipeline:
• Data Capture: Video feed from cameras inside the store.
• Preprocessing: Frame extraction, noise reduction, and normalization.
• Object Localization: AI models identify and mark objects (e.g., shoppers, products).
• Classification: Classifying objects into categories like "person," "product,"
"shopping cart."
• Post-processing: Filtering out false detections and sending alerts to security
personnel.
• Technologies Used:
• Convolutional Neural Networks (CNNs)
• YOLO (You Only Look Once)
• Faster R-CNN
• SSD (Single Shot Multibox Detector)
Key Benefits of Real-Time Object Detection
in Retail Stores
1. Theft Prevention and Detection:
• Immediate identification of suspicious behaviour (e.g., shoplifting, loitering).
• Automated alerts to store security personnel.
2. Improved Customer Experience:
• Monitoring customer interactions with products for better service.
• Identifying bottlenecks (e.g., crowded aisles) for better store management.
3. Operational Efficiency:
• Tracking product placement and inventory in real-time.
• Ensuring products are displayed correctly.
4. Employee Safety and Monitoring:
• Detecting employee behaviour in high-risk areas (e.g., backrooms, cash registers).
• Ensuring that staff follow safety protocols.
Technologies Enabling Real-Time Object
Detection
• Deep Learning Frameworks:
• TensorFlow, PyTorch, and Keras for model development.
• Hardware:
• GPUs, edge devices, and specialized hardware like TPUs for faster processing.
• Cloud vs Edge Computing:
• Cloud offers powerful processing but may introduce latency.
• Edge computing minimizes latency, ideal for real-time applications.
Problem statement
Challenges in Retail Stores:
• Theft and security breaches
• Operational inefficiencies
• Poor customer experience
Aim and Objectives
• Aim:
Develop a real-time object detection system for surveillance in retail stores.
• Objectives:
Design and implement a real-time object detection software.
Test and evaluate the system for accuracy and reliability.
Provide recommendations for improved security in retail stores.
Gather user feedback for further system improvements
S/N
REF
METHODOLOGY
RESULTS
LIMITATIONS
1
Joseph Redmon (2016)
Developed YOLO (You Only
Look Once) for real-time object
detection by framing detection
as a single regression problem.
Achieved high-speed detection
at 45 frames per second with
good accuracy.
Makes more struggles with
small object localization and
makes more localization errors
compared to other methods.
2
Ross Girshick (2014)
Introduced
R-CNN
(Regionbased Convolutional
Neural Networks) for object
detection
using
region
proposals.
Improved detection accuracy
significantly over previous
methods.
Slow processing time due to the
complex pipeline involving
multiple stages.
3
Shaoqing Ren (2015)
Developed Faster R-CNN, Achieved faster detection
speeds and higher accuracy
which integrates region
proposal networks with Fast R- compared to R-CNN.
CNN.
Still not real-time and requires
significant
computational
resources.
4
Wei Liu (2016)
Created SSD (Single Shot
MultiBox Detector) for object
detection using a single deep
neural network.
Balanced speed and accuracy,
making it suitable for real-time
applications.
Less accurate for small objects
compared to other methods.
5
Christian Szegedy (2015)
Developed Inception models
for image classification and
detection, focusing on efficient
Achieved state-of-the-art
performance in image
classification tasks.
Complex architecture and high
computational requirements.
Preliminary Literature Review
Scope of the Study
• Location and Geographical Area:
Focus on urban retail stores in developed regions with stable technology infrastructure.
• Retail Sector:
Study is limited to brick-and-mortar retail sectors such as supermarkets, fashion, and
electronics.
• Technological Scope:
Focus on existing real-time object detection models (YOLO, SSD, Faster R-CNN).
• Timeframe and Data Collection:
Data will be collected over 3 to 6 months in selected retail stores.
Significance of the study
- Detects suspicious behaviour and unauthorized objects, reducing theft and improving
safety.
- Automates surveillance, helps with inventory management and staff deployment.
- Provides insights into customer behaviour, optimizing store layout and marketing
strategies.
- Reduces reliance on manual monitoring, cutting labour costs.
- Ensures incident documentation and regulatory compliance.
Limitations of the Study
- Study limited to urban areas; may not apply to rural/remote regions.
- Focus on specific retail sectors (supermarkets, fashion); results may not apply to others.
- Assumes availability of basic tech infrastructure (e.g., cameras, network).
- Accuracy may vary depending on store layout and lighting conditions.
- Does not fully address privacy and ethical concerns.
Justification
• This study is essential due to the increasing need for real-time object detection in retail.
• It helps improve security, inventory management, and customer experience.
• With AI-driven technologies, the study provides practical insights into automating
surveillance and supporting digital transformation in retail.
Methodology
1. Literature Review:
Review existing research on object detection models (YOLO, SSD, Faster R-CNN).
2. System Deployment:
Implement real-time object detection systems in selected retail stores.
3. Data Collection:
Gather data on performance metrics, inventory levels, and security incidents.
4. Evaluation:
-Evaluate accuracy, speed, and real-time application.
5. Analysis:
Analyse the impact on security, inventory management, and customer experience.
Expected Result
• Improved accuracy and speed in object detection.
• Efficient inventory management and reduced operational costs.
• Enhanced customer behaviour analysis and personalized shopping experiences.
• Addressing privacy concerns to ensure ethical deployment.
• Customizable solutions for different retail environments.
Contribution to Knowledge
• Real-time object detection framework for retail surveillance.
• Ethical and privacy solutions for responsible AI deployment.
• Customization and adaptability for different retail environments.
• Bridging theory and practice in object detection technologies.
• Operational efficiency improvements through automated inventory and behaviour
analysis.
• Comprehensive evaluation of object detection algorithm performance in retail.
Conclusion
• Real-time object detection is transforming surveillance by improving accuracy,
reducing response times, and enhancing security.
• Challenges remain, but the future holds great promise with ongoing technological
advancements.
• Real-time object detection is a key enabler of smarter, safer environments for both
public and private sectors.
• This study provides significant contributions to enhancing retail store operations
through real-time object detection.
• The findings will support retailers in leveraging AI-driven surveillance systems to
improve security, efficiency, and customer satisfaction.
Thank you!